4,430 research outputs found
Wireless Communication using Unmanned Aerial Vehicles (UAVs): Optimal Transport Theory for Hover Time Optimization
In this paper, the effective use of flight-time constrained unmanned aerial
vehicles (UAVs) as flying base stations that can provide wireless service to
ground users is investigated. In particular, a novel framework for optimizing
the performance of such UAV-based wireless systems in terms of the average
number of bits (data service) transmitted to users as well as UAVs' hover
duration (i.e. flight time) is proposed. In the considered model, UAVs hover
over a given geographical area to serve ground users that are distributed
within the area based on an arbitrary spatial distribution function. In this
case, two practical scenarios are considered. In the first scenario, based on
the maximum possible hover times of UAVs, the average data service delivered to
the users under a fair resource allocation scheme is maximized by finding the
optimal cell partitions associated to the UAVs. Using the mathematical
framework of optimal transport theory, a gradient-based algorithm is proposed
for optimally partitioning the geographical area based on the users'
distribution, hover times, and locations of the UAVs. In the second scenario,
given the load requirements of ground users, the minimum average hover time
that the UAVs need for completely servicing their ground users is derived. To
this end, first, an optimal bandwidth allocation scheme for serving the users
is proposed. Then, given this optimal bandwidth allocation, the optimal cell
partitions associated with the UAVs are derived by exploiting the optimal
transport theory. Results show that our proposed cell partitioning approach
leads to a significantly higher fairness among the users compared to the
classical weighted Voronoi diagram. In addition, our results reveal an inherent
tradeoff between the hover time of UAVs and bandwidth efficiency while serving
the ground users
Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks
An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet-of-Things (IoT) users, by optimizing offloading decision, transmission power, and resource allocation in the large-scale mobile-edge computing (MEC) system. Toward this end, a deep reinforcement learning (DRL)-based solution is proposed, which includes the following components. First, a related and regularized stacked autoencoder (2r-SAE) with unsupervised learning is applied to perform data compression and representation for high-dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Second, we present an adaptive simulated annealing approach (ASA) as the action search method of DRL, in which an adaptive h -mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Third, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. The numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks
Wireless Communication using Unmanned Aerial Vehicles (UAVs): Optimal Transport Theory for Hover Time Optimization
In this paper, the effective use of flight-time constrained unmanned aerial
vehicles (UAVs) as flying base stations that can provide wireless service to
ground users is investigated. In particular, a novel framework for optimizing
the performance of such UAV-based wireless systems in terms of the average
number of bits (data service) transmitted to users as well as UAVs' hover
duration (i.e. flight time) is proposed. In the considered model, UAVs hover
over a given geographical area to serve ground users that are distributed
within the area based on an arbitrary spatial distribution function. In this
case, two practical scenarios are considered. In the first scenario, based on
the maximum possible hover times of UAVs, the average data service delivered to
the users under a fair resource allocation scheme is maximized by finding the
optimal cell partitions associated to the UAVs. Using the mathematical
framework of optimal transport theory, a gradient-based algorithm is proposed
for optimally partitioning the geographical area based on the users'
distribution, hover times, and locations of the UAVs. In the second scenario,
given the load requirements of ground users, the minimum average hover time
that the UAVs need for completely servicing their ground users is derived. To
this end, first, an optimal bandwidth allocation scheme for serving the users
is proposed. Then, given this optimal bandwidth allocation, the optimal cell
partitions associated with the UAVs are derived by exploiting the optimal
transport theory. Results show that our proposed cell partitioning approach
leads to a significantly higher fairness among the users compared to the
classical weighted Voronoi diagram. In addition, our results reveal an inherent
tradeoff between the hover time of UAVs and bandwidth efficiency while serving
the ground users
- …